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The workflow scheduling problems involve the task-resource mapping satisfying some functional and nonfunctionalquality of service. Workflow applications require high computational power and often involve a large amount of data transfer from one place to another. Furthermore, due to dependencies existed among tasks; schedules must be brought forth according to given precedence constraints. Cloud computing is a new business-oriented platform service that facilitates an infinite number of services by providing heterogeneous, virtualized resources to users based on a pay-as-you-go model with the distinctive quality of service (QoS) Constraints. Due to its market-oriented approach, conventional workflow scheduling strategies are facing new challengeslike on-demand payment,unprecendented openness and autonomy. This paperpresents anadaptive privileged multi-objective workflow scheduling algorithm (APMWSA) which optimally run the workflow execution process for minimization of total cost and makespan. This algorithm uses the concept of novel adaptive elite-based particle swarm optimization (NAEB-PSO) for taskresource mapping.A comparative study of presented algorithm is also made with some existing algorithms.
kothyari et al. (Thu,) studied this question.